Course Description

This course introduces students to advanced problem-solving techniques inspired by nature and artificial intelligence. It focuses on intelligent algorithms that can efficiently tackle complex optimization and search problems where traditional methods fail or are computationally expensive.

Students will learn how these algorithms mimic natural behaviors — such as evolution, swarm cooperation, and adaptive learning — to discover near-optimal solutions in dynamic environments.

Course Objectives

By the end of this course, students will be able to:

1. Understand the principles behind heuristic and meta-heuristic algorithms.
2. Implement and analyze intelligent algorithms such as Genetic Algorithms (GA) and Ant Colony Optimization (ACO).
3. Apply these algorithms to real-world optimization problems, including the Knapsack Problem and Longest Common Subsequence (LCS).
4. Compare the performance of intelligent algorithms with traditional deterministic approaches.
5. Design hybrid and adaptive approaches for enhanced performance and solution quality.

Main Topics

• Introduction to Intelligent and Nature-Inspired Algorithms
• Genetic Algorithms (GA) – Selection, crossover, mutation, and fitness evaluation
• Ant Colony Optimization (ACO) – Path construction, pheromone update, and convergence
• Knapsack Problem – Optimization of resource allocation
• Longest Common Subsequence (LCS) – Sequence alignment using intelligent search
• Performance evaluation and parameter tuning
• Hybrid intelligent systems and real-world applications